Euclidean Distance Clustering in Spectroscopy, with Applications for Polymorphs, Formulated Products, and Other Areas
2 Dec 2014

Finding which samples are similar to each other is a task often encountered in many areas of chemistry, from looking at polymorphs and salt forms in preformulation to determining similar groupings of data in competitive analysis or formulated products. There are many approaches to clustering data; this poster will look at the Euclidean Distance algorithm, some of the limitations in this approach and the data review steps required when clustering data.

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